A novel stacking ensemble model for predicting discharge coefficient of submerged multi parallel radial gates

Enhancing the precision of discharge coefficient (C_d) prediction holds paramount importance for effective Water distribution control. Calculating the C_d for radial gates is often complex, with existing methods frequently depending on intricate procedures and underlying assumptions. This study introduces a deep learning-based stacking ensemble model for C_d prediction. The proposed model comprises a dual-layer structure. Four machine learning algorithms are exploited as baseline models. The Meta model employed long short-term memory (LSTM) with attention mechanism to amalgamate the outputs from the base models and assign sufficient weight to each base model. The spatial attention mechanism effectively highlighted relevant patterns within the data. The proposed model achieved an impressive root mean square error of 0.0175. The ensemble model outperformed existing longstanding models. The proposed system holds substantial strategic importance, enabling optimal water resource management.